Systematic Research on Teacher Growth Theory of Multiple Intelligences Based on Association Rule Mining

Abstract

Association rule mining is an important topic in big data analytics, discovering useful knowledge hidden in massive amounts of data and uncovering correlations, dependencies and causal structures between data items to provide support for aiding decision-making. The multiple intelligences theory includes eight relatively independent yet interconnected intelligences, emphasising the diversity of human abilities and playing a positive role in influencing teacher growth. Using association rule mining as a basis and combining it with multiple intelligences theory to study teacher development can help cultivate teachers with creative awareness and innovative spirit. This paper investigates the principle and algorithmic process of the Apriori algorithm, constructs the general structure of multiple intelligences theory, and proposes strategies for multiple intelligences teacher growth under association rule mining to provide theoretical support for teacher growth research.